Training machine learning models to exclude ambiguous data samples

ABSTRACT

Techniques for training machine learning models for improved accuracy at classifying medical imaging data sets by trimming ambiguous samples from training data sets are described herein. In some embodiments, a machine learning model is trained using a data set, where a subset of the data set comprises data with a conflict between a first label based on an expert opinion and a second label based on a ground truth based on a medical examination. During some epochs of training the machine learning model, loss values for each data sample in the epoch are compared against a loss threshold, with data samples with corresponding loss values that exceed the loss threshold that also belong to the subclass trimmed from the data set for subsequent epochs of training. The loss threshold for the next epoch is then updated based on loss values of the trimmed data set.

BACKGROUND

The present invention relates generally to the field of training machinelearning models, and more particularly to training machine learningmodels for medical image classification.

Machine learning (ML) is the study of computer algorithms whichautomatically improve through experience. It is typically viewed as asubset of artificial intelligence (AI). Machine learning algorithmstypically construct a mathematical model based on sample data, sometimesknown as “training data”, in order to determine predictions or decisionswithout being specifically programmed to do so.

Loss is a value indicative of how inaccurate a ML model's prediction wason a single example. If the model's prediction is perfect, the lossvalue is zero; otherwise, the loss value is greater. The objective oftraining a model is to find a set of weights and biases that have as lowof a loss value as possible, on average, across all examples. The batchsize is a quantity of samples processed prior to updating the ML model.The number of epochs is the quantity of complete passes through thetraining data set.

SUMMARY

According to an aspect of the present invention, there is a method,computer program product and/or system that performs the followingoperations (not necessarily in the following order): (i) receiving amachine learning (ML) model trained on a training data set that includesat least one subclass of data samples where disagreement exists betweenmanual annotations for the samples and a related externally groundeddetermination; (ii) determining a set of loss values for data samples ofthe training data set in an epoch of training the ML model; (iii)determining a loss threshold; and (iv) trimming at least one data samplebelonging to the at least one of said subclass from the training dataset based on the loss threshold and the loss value(s) of the set of lossvalues corresponding to the at least one data sample.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram view of a first embodiment of a systemaccording to the present invention;

FIG. 2 is a flowchart showing a first embodiment method performed, atleast in part, by the first embodiment system;

FIG. 3 is a block diagram showing a machine logic (for example,software) portion of the first embodiment system;

FIG. 4 is a screenshot view generated by the first embodiment system;

FIG. 5 is a screenshot view showing four example mammogram imagescorresponding to several subclasses of malignancy detection relatedimages for a second embodiment;

FIG. 6 is a screenshot view showing five example mammogram imagescorresponding to several subclasses of density related images for athird embodiment;

FIG. 7 is a table showing sample data sets used in an example of thesecond embodiment;

FIG. 8 is a table showing sample data sets used in an example of thethird embodiment;

FIG. 9 is a table showing results comparing between the secondembodiment and state of the art methods; and

FIG. 10 is a table showing results comparing between the thirdembodiment and state of the art methods.

DETAILED DESCRIPTION

Some embodiments of the present invention are directed to techniques fortraining machine learning models for improved accuracy at classifyingmedical imaging data sets by trimming ambiguous samples from trainingdata sets. In some embodiments, a machine learning model is trainedusing a data set, where a subset of the data set comprises data with aconflict between a first label based on an expert opinion and a secondlabel based on a ground truth based on a medical examination. Duringsome epochs of training the machine learning model, loss values for eachdata sample in the epoch are compared against a loss threshold, withdata samples with corresponding loss values that exceed the lossthreshold that also belong to the subclass trimmed from the data set forsubsequent epochs of training. The loss threshold for the next epoch isthen updated based on loss values of the trimmed data set.

This Detailed Description section is divided into the followingsubsections: (i) The Hardware and Software Environment; (ii) ExampleEmbodiment; (iii) Further Comments and/or Embodiments; and (iv)Definitions.

I. The Hardware and Software Environment

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (for example, lightpulses passing through a fiber-optic cable), or electrical signalstransmitted through a wire.

A “storage device” is hereby defined to be any thing made or adapted tostore computer code in a manner so that the computer code can beaccessed by a computer processor. A storage device typically includes astorage medium, which is the material in, or on, which the data of thecomputer code is stored. A single “storage device” may have: (i)multiple discrete portions that are spaced apart, or distributed (forexample, a set of six solid state storage devices respectively locatedin six laptop computers that collectively store a single computerprogram); and/or (ii) may use multiple storage media (for example, a setof computer code that is partially stored in as magnetic domains in acomputer's non-volatile storage and partially stored in a set ofsemiconductor switches in the computer's volatile memory). The term“storage medium” should be construed to cover situations where multipledifferent types of storage media are used.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

As shown in FIG. 1, networked computers system 100 is an embodiment of ahardware and software environment for use with various embodiments ofthe present invention. Networked computers system 100 includes: machinelearning (ML) training subsystem 102 (sometimes herein referred to, moresimply, as subsystem 102); client subsystems 104, 106, 108, 110, 112;and communication network 114. Subsystem 102 includes: ML trainingcomputer 200; communication unit 202; processor set 204; input/output(I/O) interface set 206; memory 208; persistent storage 210; display212; external device(s) 214; random access memory (RAM) 230; cache 232;and program 300.

Subsystem 102 may be a laptop computer, tablet computer, netbookcomputer, personal computer (PC), a desktop computer, a personal digitalassistant (PDA), a smart phone, or any other type of computer (seedefinition of “computer” in Definitions section, below). Program 300 isa collection of machine readable instructions and/or data that is usedto create, manage and control certain software functions that will bediscussed in detail, below, in the Example Embodiment subsection of thisDetailed Description section.

Subsystem 102 is capable of communicating with other computer subsystemsvia communication network 114. Network 114 can be, for example, a localarea network (LAN), a wide area network (WAN) such as the Internet, or acombination of the two, and can include wired, wireless, or fiber opticconnections. In general, network 114 can be any combination ofconnections and protocols that will support communications betweenserver and client subsystems.

Subsystem 102 is shown as a block diagram with many double arrows. Thesedouble arrows (no separate reference numerals) represent acommunications fabric, which provides communications between variouscomponents of subsystem 102. This communications fabric can beimplemented with any architecture designed for passing data and/orcontrol information between processors (such as microprocessors,communications and network processors, etc.), system memory, peripheraldevices, and any other hardware components within a computer system. Forexample, the communications fabric can be implemented, at least in part,with one or more buses.

Memory 208 and persistent storage 210 are computer-readable storagemedia. In general, memory 208 can include any suitable volatile ornon-volatile computer-readable storage media. It is further noted that,now and/or in the near future: (i) external device(s) 214 may be able tosupply, some or all, memory for subsystem 102; and/or (ii) devicesexternal to subsystem 102 may be able to provide memory for subsystem102. Both memory 208 and persistent storage 210: (i) store data in amanner that is less transient than a signal in transit; and (ii) storedata on a tangible medium (such as magnetic or optical domains). In thisembodiment, memory 208 is volatile storage, while persistent storage 210provides nonvolatile storage. The media used by persistent storage 210may also be removable. For example, a removable hard drive may be usedfor persistent storage 210. Other examples include optical and magneticdisks, thumb drives, and smart cards that are inserted into a drive fortransfer onto another computer-readable storage medium that is also partof persistent storage 210.

Communications unit 202 provides for communications with other dataprocessing systems or devices external to subsystem 102. In theseexamples, communications unit 202 includes one or more network interfacecards. Communications unit 202 may provide communications through theuse of either or both physical and wireless communications links. Anysoftware modules discussed herein may be downloaded to a persistentstorage device (such as persistent storage 210) through a communicationsunit (such as communications unit 202).

I/O interface set 206 allows for input and output of data with otherdevices that may be connected locally in data communication with MLtraining computer 200. For example, I/O interface set 206 provides aconnection to external device set 214. External device set 214 willtypically include devices such as a keyboard, keypad, a touch screen,and/or some other suitable input device. External device set 214 canalso include portable computer-readable storage media such as, forexample, thumb drives, portable optical or magnetic disks, and memorycards. Software and data used to practice embodiments of the presentinvention, for example, program 300, can be stored on such portablecomputer-readable storage media. I/O interface set 206 also connects indata communication with display 212. Display 212 is a display devicethat provides a mechanism to display data to a user and may be, forexample, a computer monitor or a smart phone display screen.

In this embodiment, program 300 is stored in persistent storage 210 foraccess and/or execution by one or more computer processors of processorset 204, usually through one or more memories of memory 208. It will beunderstood by those of skill in the art that program 300 may be storedin a more highly distributed manner during its run time and/or when itis not running. Program 300 may include both machine readable andperformable instructions and/or substantive data (that is, the type ofdata stored in a database). In this particular embodiment, persistentstorage 210 includes a magnetic hard disk drive. To name some possiblevariations, persistent storage 210 may include a solid state hard drive,a semiconductor storage device, read-only memory (ROM), erasableprogrammable read-only memory (EPROM), flash memory, or any othercomputer-readable storage media that is capable of storing programinstructions or digital information.

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of theinvention. However, it should be appreciated that any particular programnomenclature herein is used merely for convenience, and thus theinvention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

II. Example Embodiment

As shown in FIG. 1, networked computers system 100 is an environment inwhich an example method according to the present invention can beperformed. As shown in FIG. 2, flowchart 250 shows an example methodaccording to the present invention. As shown in FIG. 3, program 300performs or control performance of at least some of the methodoperations of flowchart 250. This method and associated software willnow be discussed, over the course of the following paragraphs, withextensive reference to the blocks of FIGS. 1, 2 and 3.

Processing begins at operation S255, where machine learning (ML) modeldata store module (“mod”) 302 receives a ML model and training data setdata store mod 304 receives a corresponding training data set. Thetraining data set and ML model are received from client 104 of FIG. 1and are communicated over network 114 to subsystem 102. In thissimplified embodiment, the training data set includes two subclasses ofdata samples: (i) clear data samples stored in clear data samples datastore mod 306; and (ii) ambiguous data samples stored in ambiguoussamples data store mod 308. An ambiguous data sample is a data samplewith a manual annotation, usually determined by an expert, and acorresponding ‘externally grounded determination,’ also typicallydetermined by an expert, where the annotation and the externallygrounded determination disagree. An externally grounded determination istypically a conclusion or result determined by an expert concerningsubject matter that is also the subject of the manual annotation butreached using different methodology that is more firmly grounded indirectly observable evidence. For example, in this simplifiedembodiment, the training data set is a collection of four grayscalemammogram (MG) images, with manual annotations set by the radiologistwho reviewed the MG images, and the externally grounded determinationlabel (when present) set by the doctor who evaluated a biopsy samplefrom the patient subject of the MG image. A data sample will only havean externally grounded determination label when a biopsy is performed,which is typically only performed when the radiologist indicated apositive result in the MG image. By contrast, a data sample in the clearclass either has no externally grounded determination or concurrencebetween the manual annotation and an externally grounded determination.

The first data sample image has an annotation of negative, meaning thatthe patient subject of the image was not sent for a biopsy, resulting inno externally grounded determination, making the first image a member ofthe clear class of data samples due to the absence of conflict betweenthe manual annotation and an externally grounded determination. Thesecond data sample image has an annotation of positive, and anexternally grounded determination of a positive biopsy indicatingmalignancy. With concurrence between the manual annotation and theexternally grounded determination, the second image belongs to the clearclass of data samples. The third data sample image has an annotation ofpositive and an externally grounded determination of a negative biopsyindicative of a benign lesion. With disagreement between the manualannotation of the third image and the externally grounded determinationof the third image, the third image belongs to the ambiguous class ofdata samples. The fourth data sample image has an annotation of positiveand an externally grounded determination of a negative biopsy indicativeof a benign lesion. Similarly, as with the third image, the fourth imagealso belongs to the ambiguous class of data samples. The ML model istrained to classify data sample MG images samples into one of twoclasses: (i) the image indicate a negative result, or an absence ofcancer indicators; (ii) the image indicates a positive result, or thepresence of cancer indicators. In alternative embodiments, a trainingdata set includes a significantly larger number of data samples.

Processing proceeds to operation S260, where loss value determinationmod 310 determines a loss value of a first epoch. In this simplifiedembodiment, the loss value is calculated using a cross-entropy functionfor each individual data sample. The first epoch includes all four datasamples from the training data set. After the ML model classifies eachdata sample, the loss function returns a loss value indicative of the MLmodel's accuracy. In this simplified embodiment, the loss value for eachdata sample in this epoch is listed as follows: (i) the first datasample loss value is 2.3; (ii) the second data sample loss value is 4.5;(iii) the third data sample loss value is 3.6; and (iv) the fourth datasample loss value is 6.8.

Processing proceeds to operation S265, where threshold determination mod312 determines a loss threshold for trimming samples of an ambiguousclass from the training data set. In this simplified embodiment, theloss threshold is based on the average loss value determined at S260.The average of the loss values (2.3, 4.5, 3.6 and 6.8) is 4.3, which isthe loss threshold. In alternative embodiments, other types of lossthresholds may be used. For example, the loss threshold can be anotherclassifier trained to identify data samples belonging to the ambiguousclass, or E^(k)[L]/2.

Processing proceeds to operation S270, where subset trimming mod 314trims a subset of the ambiguous class from the data set based on thethreshold. In this simplified embodiment, data samples of the ambiguousclass with loss values above the loss threshold are trimmed from thetraining data set to create a trimmed data set. Two data samples aremembers of the ambiguous class: (i) the third data sample image; and(ii) the fourth data sample image. The loss value for the third datasample image, 3.6, is beneath the loss threshold, 4.3, so it is nottrimmed from the training data set at this time. The loss value for thefourth data sample image, 6.8, is above the loss threshold, 4.3, so itis trimmed from the training data set at this time. The loss value forthe second data sample image, 4.5, is above the loss threshold, 4.3, butit is not removed from the training data set because it does not belongto the ambiguous class. This data sample image is valuable in trainingthe ML model towards improved accuracy in its classification tasks.

Processing proceeds to S275, where threshold updater mod 316 updates theloss threshold. In this simplified embodiment, the loss threshold isupdated to be the average of the loss values from the first epoch of thedata sample images, excluding any loss values corresponding to trimmedambiguous class data sample images. At S270, the fourth data sampleimage was trimmed from the training data set, so the corresponding lossvalue is excluded from contributing to the updated loss threshold. Withloss values from only the first data sample image (2.3), second datasample image (4.5), and third data sample image (3.6) remaining, theaverage loss value, and thus the updated loss threshold, is 3.5.

Processing proceeds to operation S280, where loss value determinationmod 310 determines a loss value on the trimmed data set over an epoch.In this simplified embodiment, the loss value is again calculated usinga cross-entropy function for each individual data sample. This epochincludes data sample images from the training data set, absent any datasample images from the ambiguous class previously excluded, resulting inonly the first data sample image, second data sample image, and thirddata sample image included in the training data set for this epoch.After the ML model classifies each data sample image, the loss functionreturns a loss value indicative of the ML model's accuracy. In thissimplified embodiment, the loss value for each data sample in this epochis listed as follows: (i) the first data sample loss value is 1.9; (ii)the second data sample loss value is 4.0; and (iii) the third datasample loss value is 3.6.

Processing proceeds to operation S285, where subset trimming mod 314trims another subset of the ambiguous class from the trimmed data setbased on the updated threshold. In this simplified embodiment, theupdated loss threshold, set at S275, is 3.5 Similar to S270, anytraining data set images in the trimmed training data set belonging tothe ambiguous class with a loss value above the updated loss thresholdare trimmed from the training data set, creating a second trimmedtraining data set. In this simplified embodiment, only one data sampleimage belonging to the ambiguous class, the third data sample image,remains in the trimmed training data set. The loss value in this epochfor the third data sample image is 3.6, which is above the updated lossthreshold. Since the third training data sample image belongs to theambiguous class, and has a loss value for this epoch above thepreviously updated loss threshold, it is trimmed from the training dataset, resulting in a second trimmed training data set which includes onlythe first data sample and the second data sample.

Subsequent epochs, if desired, may begin after updating the threshold asin S275, then proceeding through S280 and S285. When the ambiguous classdata samples have been sufficiently trimmed from the training data set,with a finally trimmed training data set remaining, the ML model maythen perform epochs of training using the finally trimmed training dataset. Trimming the ambiguous class data samples from the data setfacilitates a more accurate machine learning model by virtue of theambiguous class data samples introduce inaccuracy to the machinelearning model.

III. Further Comments and/or Embodiments

Some embodiments of the present invention recognize the following facts,potential problems and/or potential areas for improvement with respectto the current state of the art: (i) deep neural networks rely on largeamounts of data, some of which may be noisy or erroneous; (ii) forexample, for supervised tasks this data is annotated by human experts,and as a result is prone to human error; (iii) in medical applications,images are often only one of the many modalities of data used fordiagnosis; (iv) suspicious findings on images are often negated orconfirmed on the basis of patient clinical condition and the results ofother tests; (v) this amounts to a structural noise problem for medicalimage annotations; (vi) Medical imaging analysis is usually performed bya medical expert; (vii) for example, a radiologist will analyze amammography image, or any other modality and produce an expert opinion;(viii) in cases where the expert believes that there is a medicalcondition, the patient is sent for further inspection; (ix) inmammography for example, the image is given a score; (x) if the score ishigh, the patient will be sent for a biopsy, where a final decision willbe made; (xi) therefore, even instances where an expert has mademistakes, medical imaging pertaining to mammograms can still becategorized into 3 classes; (xii) the first, Negative (N)—where theexpert thought the patient is healthy and she is indeed healthy; (xiii)the second, Positive (P)—where the expert thought the patient should gothrough further medical examination and she was indeed un-healthy; (xiv)the third, Undetermined (U)—where the expert thought the patient shouldgo through further medical examination but she is healthy; (xv) thisthird category is elusive, since it is unclear whether the feature whichindicates malignancy is indeed in the image; and (xvi) in some of theimages it is present, and in some it is not.

Some embodiments of the present invention recognize the following facts,potential problems and/or potential areas for improvement with respectto the current state of the art: (i) the availability of enormousamounts of data and the constant improvement in computing power, havemade deep-learning one of the most common machine learning techniques;(ii) given enough data, neural networks provide state-of-the-artresults, and can easily outperform traditional computer-visionalgorithms; (iii) however, network training relies on huge amounts ofdata to reach the levels of performance required; (iv) moreover, forfully supervised tasks all of this data must be annotated; (v)mammography (MG) is the primary imaging modality currently used as ascreening tool to detect early breast cancer in women; (vi) the resultsof the examination are analyzed by a radiologist, and approximately 10%of the screening patients are sent for further medical examination; and(vii) the data from these examinations is one of the main sources beingused to train deep learning networks to identify breast cancer.

Some embodiments of the present invention may include one, or more, ofthe following operations, features, characteristics and/or advantages:(i) a mechanism for removing undetermined images which harm deep neuralnetwork (DNN) training; (ii) taking a pretrained network on a datasetand continue fine-tuning it with the same dataset by removing imagesbelonging to the undetermined class from training; (iii) training a DNNusing a dataset containing the above three classes (P, N, and U); (iv)loading the model from stage 1, and according to some base rule relatingto the loss of the training data continue training with the dataset P, Nand a subset of U according to the rule; (v) updating the rule aftereach epoch/batch; (vi) one example rule is: all images above theaverage/median of the loss function calculated on the entire dataset;(vii) in an alternative embodiment, the rule can be a second networkaimed at classifying images in the undetermined category; (viii) thespecial definition of the undetermined subclass; (ix) use loss value todetermine exclusion from a training set; (x) use an online method; (xi)defining the ambiguous class (AC), and treating it separately duringtraining; (xii) a technique for improving machine-learning modeltraining by identifying a sub-class which is defined as all sampleswhich have a disagreement between their manual annotation and anexternal ground truth relating to the sample, and removing this classduring the training process; and (xiii) this technique includes: (a)calculating the loss value over one epoch, (b) determining a lossthreshold in relation to the calculated loss, (c) any sample from the ACwhich has a value above the previously calculated threshold is ignored,and (d) at the end of an epoch update the loss threshold.

Some embodiments of the present invention may include one, or more, ofthe following operations, features, characteristics and/or advantages:(i) a novel training regime for classification tasks particularlyeffective for to the structure and challenges associated with medicalimaging data; (ii) a mix of noisy loss trimming with hard examplesmining; (iii) use the novel approach only on a subset of the data,defined as ambiguous; (iv) in one example, the ambiguous subset isdefined as benign lesions; (v) this subset is: (a) confirmed by biopsyresults, and (b) was sent for biopsy due to its unclear visualcharacteristic; (vi) hence the subset is treated as ambiguous; (vii)experimented on cancer classification for a dataset of breastmammography; (viii) in addition, there is another scenario where theambiguous class appears in the breast tissue density classificationtask; (ix) this approach shows an improvement in classificationperformance compared to other state-of-the-art methods; (x) perform thetechnique on a distinct sub-class which is of special importance to themedical field; (xi) the identification of this sub-class is not trivial;and (xii) the threshold chosen for image selection is based on the lossstatistics of the specific dataset instead of a constant percent of theimages.

Some embodiments of the present invention may include one, or more, ofthe following operations, features, characteristics and/or advantages:(i) annotations for MG datasets originate from two sources; (ii) humanannotation based on images only; (iii) final results of further medicalexamination (for example, biopsy results); (iv) there is a uniquesubclass of breast MG imaging datasets that is defined as the ambiguousclass (AC); (v) this subclass is characterized by the fact that itsannotation label is based solely on the image, and differ from the finalmedical result; (vi) in the screening process, radiologists are requiredto mark each patient's study for recall or no recall; (vii) if a patientis recalled, but the biopsy results are benign, the radiologist'ssuspicion is refuted; (viii) for cancer detection in MG, the benignimages where the radiologist marked the study for recall are identifiedas being ambiguous; (ix) FIG. 5 presents MG images 500 from threesubclasses of the MG classification task—normal, benign (AC), andmalignant; and (x) regarding FIG. 5, the findings have subtledifferentiation, and annotation may be challenging even for expertradiologists.

Some embodiments of the present invention may include one, or more, ofthe following operations, features, characteristics and/or advantages:(i) this AC can be identified in another use-case of MG analysis; (ii)for each MG analysis, the radiologist is also required to grade thedensity of the breast; (iii) breast density is categorized into fourranks: A, B, C, and D, where A is very low density, and D very highdensity; (iv) in this use-case there is no external ground truth (suchas a biopsy confirming malignancy or benign status) and the rankingsvary significantly between different experts; (v) due to the largedisagreement, this technique was explored using a dataset that wasannotated by five expert radiologists (each with at least five years ofexperience) and includes a report from the hospital where the exam wastaken; (vi) We in this use-case scenario the AC is defined as the subsetof the data where there is a disagreement between the report and theannotators; (vii) FIG. 6 presents MG images 600 from sub-classes of theMG breast density (BD) classification scenario—A, B, C, D; (viii) imagenumber 605 is an example for the AC with disagreement between B and Csub-classes; (ix) the AC may be thought of as noise in the labels of thedataset, since some features that appear in the image may be misleadingfor the classification task; (x) there is no method for knowing inadvance which of the images contain misleading images, and which do not;(xi) this noise can harm the training process and hence reduce theevaluation results of the underlying ML model; (xii) although thisdefinition of AC does not fit the definition of flip-labels as it is nota-priori clear whether the relevant features for the classification taskappear in the image; and (xiii) this approach distinguishes subclassesfor loss evaluation purposes.

Some embodiments of the present invention may include one, or more, ofthe following operations, features, characteristics and/or advantages:(i) a novel approach for tackling the unique noise of the AC subset;(ii) this approach consists of dynamically removing training images thatbelong to the AC subclass; (iii) the images to be removed are chosen atthe beginning of each epoch, and are chosen in relation to thestatistics of the epoch loss; (iv) this approach is sensitive to theinternal structure of the AC; (v) if all samples of the AC have a verylow loss relative to the entire sample loss statistics, no image will beremoved, while in other cases, a large portion may be removed; (vi) thisapproach can be shown through two different classification exampletasks: (a) MG classification of malignant versus non-malignant where theAC is the benign class, and (b) MG classification of low breast densityversus high breast density (A, B versus C, D) where the AC is the subsetof the data in which there is disagreement between the annotators andthe medical report; (vii) each of the example tasks use three trainingsets with an increasing amount of AC to fully explore the impact of thisunique class on the prediction of the network; (viii) this novelapproach shows superior results in comparison of using the entiretraining set; (ix) this novel approach exploits the gap between apatient diagnosis at the end of the diagnostic workup and the outcome ofone specific examination done during this workup; (x) identifying theambiguous subset, which can be relevant for general medical examinationstasks; (xi) creating a unique model for training that removes onlyambiguous images; (xii) setting a dynamic threshold for selecting imagesto be removed; and (xiii) the threshold depends on the loss statisticsof the entire dataset and is therefore sensitive to the internalstructure of the AC.

Some embodiments of the present invention may include one, or more, ofthe following operations, features, characteristics and/or advantages:(i) one goal is to teach a classifier to capture the samples belongingto the AC and selectively ignore a sub-sample of them during training;(ii) this task is conceptually similar to other methods for trimmingnoisy labels, except that here there is a-priori knowledge about thedataset; (iii) this enables the ability to apply the trimmingspecifically to the unclear/ambiguous class, rather than trimming thefull dataset, which may remove good samples from other classes; and (iv)this is one focus for the trim loss technique.

Some embodiments of the present invention leverage the followingequations in executing focal trim loss according to the embodiments ofthe present invention:

Let {

,

} be a set of input image samples and their corresponding labels thatsome of which belong to an ambiguous class C_(AC) and the rest to aclear class C_(CL), such that: {x,

}∈{

,

}={

_(C) _(CL) ,

_(C) _(CL) }∪{

_(C) _(AC) ,

_(C) _(AC) }. θ is defined as the model's parameters and L_(i)(θ) as thecross-entropy loss function on sample

.

$\begin{matrix}{{{Equation}{\mspace{11mu}\;}1}\mspace{574mu}} & \; \\{L_{K}^{FT} = {\frac{1}{\overset{\sim}{N}}\left( {{\sum_{i \in C_{CL}}L_{i}} + {\sum_{i \in C_{AC}}{\chi_{\phi\; k}\left( L_{i} \right)}}} \right)}} & \left( {{EQ}.\mspace{14mu} 1} \right) \\{{{where}\mspace{14mu}{\chi_{\phi}(x)}{\mspace{11mu}\mspace{11mu}}{is}\mspace{14mu}{defined}\mspace{14mu}{as}},} & \; \\{{{Equation}\mspace{14mu} 2}\mspace{580mu}} & \; \\{{\chi_{\phi}(x)} = \left\{ {\begin{matrix}{x,{x < \phi}} \\{0,{else}}\end{matrix},} \right.} & \left( {{EQ}.\mspace{14mu} 2} \right)\end{matrix}$

and Ñ is the number of samples which were not removed in EQ. 1 due to EQ2. The threshold ϕ_(k) is updated at the end of each epoch, and isdependent on the statistics of the loss of the previous epoch. Thedetails of this dependency need to be set separately for each dataset,as the internal structure of the AC may vary.

Some embodiments of the present invention may include one, or more, ofthe following operations, features, characteristics and/or advantages:(i) for the medical image classification tasks, a customizedInception-ResNet-V2 architecture can be used; (ii) an example input tothe network is a grayscale image resized to 1024×512; (iii) in someexamples the network was composed of 14 Inception-Resnet blocks that arefed to a global max pooling (GMP) layer followed by two fully connectedlayers and a softmax layer; (iv) in some examples the CNN models aretrained on an IBM PowerAI machine with Nvidia Tesla-V100-16G GPU; (v)also using an Adam optimizer with a learning rate of 10-4; (vi) an 12regularization with a decay parameter of 10-4; (vii) the networkconverged after 60 epochs, which took approximately 1 day; (viii) thethreshold selection was treated as a hyper-parameter that requirestuning; (ix) it was not a goal to remove a fixed number of samples,rather only those samples that deviate from the loss statistics; (x)hence, after parameter-tuning the threshold ϕ was set as E^(k) [L]/2;(xi) the training was done in two phases; (xii) first training the modelwith all the images until it converges and then trimming images; (xiii)since the trimming threshold is set relative to the average loss value,the first stage is needed for the loss values to stabilize; and (xiv)otherwise the images that were removed would not necessarily be the onesthat harm the training.

Some embodiments of the present invention may include one, or more, ofthe following operations, features, characteristics and/or advantages:(i) two different datasets for two different MG classification tasks:(a) malignancy, and (b) breast density; (ii) for the malignancyclassification, the dataset consists of MG images from two privateinstitutes; (iii) restricting the training dataset to one institute inorder to avoid uncontrolled bias; (iv) the evaluation set was from theother institute; (v) its distribution was similar to that in thescreening population; (vi) the ground truth label was based on thepathology report of the biopsied breasts; (vii) patients were labeled‘Positive’ if their breast biopsy results was ‘Malignant’; (viii)patients with ‘Benign’ breast biopsy results or with no biopsy werelabeled as ‘Negative’; (ix) the baseline training set consisted of 5029mammogram images from 1633 patients with no benign lesions; (x) 80% ofthe samples were used in training; (xi) the other 20% were held out forvalidation; (xii) three datasets were used for training; (xiii) 10%, and25% of benign images were added to the baseline data set to create thesecond and third datasets, where the 10% and 25% form the AC of thosedatasets; (xiv) the evaluation set contained 6636 mammogram images from3975 patients; (xv) dataset distribution in the training and evaluationcohorts are given in table 700 of FIG. 7; (xvi) for the breast densityclassifier three different training sets are used with varyingpercentages of ambiguous samples; (xvii) the baseline set contained noambiguous samples, with 10%, and 25% added to the second and thirdtraining sets respectively; (xviii) all of the resulting models wereevaluated on a test set with a fixed density distribution; (xix) theground truth was defined as the density value in the report for thetraining set and as the majority vote among the annotators for the testset; (xx) patients were labeled ‘Low’ if their breast density annotationwas ‘A’ or ‘B’; (xxi) patients with ‘C’ or ‘D’ breast density annotationwere labeled as ‘High’; and (xxii) an example of breast density data issummarized in table 800 of FIG. 8.

Some embodiments of the present invention may include one, or more, ofthe following operations, features, characteristics and/or advantages:(i) this approach contends that the AC is unique and should be trimmedseparately; (ii) to quantify the impact of the Focal Trim Loss method, aclassifier is trained to classify malignant versus non-malignant MGimages; (iii) for this task, benign images are used as the AC; (iv)compare the results we obtained using different techniques; (v) a firsttechnique is the Baseline classifier, which uses all training sampleswith no modifications; (vi) a second technique is a Trim all technique,which trims all images above a specific threshold; (vii) a thirdtechnique is the Focal trim loss, which only trims images belonging tothe AC; (viii) the trim all and the focal trim loss techniques trainingwere done in two phases according previously discussed methodology; (ix)to evaluate the focal trim loss technique, training sets with a growingnumber of benign samples were used; (x) the results are listed in Table900 of FIG. 9; (xi) focal trim loss yields consistently better resultsthan any of the other techniques; (xii) an average improvement of 3points in Area Under the Receiver Operating Characteristics (AUROC)values relative to baseline training; (xiii) for the baselineclassifier, adding benign samples to the training data set lead to aninitial decrease in the results compared to no benign samples; (xiv)this result is counter intuitive since benign samples are part of thetest set; (xv) nevertheless, this supports the assumption that samplesfrom the benign class may harm the training process, and should be usedwith caution; and (xvi) using the trim all method, slightly improvedAUROC results are observed compared to the baseline results and the NoBenign set, but are still inferior to the focal trim loss technique.

Some embodiments of the present invention may include one, or more, ofthe following operations, features, characteristics and/or advantages:(i) using our focal trim loss for other applications related to medicalimaging; (ii) breast density classification is an interesting use-casewhere AC is useful, although it diverges from the original definition;(iii) training breast density classifiers with varying numbers ofambiguous cases as described in table 800 of FIG. 8; (iv) evaluated themon a test set following the population density distribution of (10%,40%, 40%, 10%) for classes (A, B, C, D); (v) table 1000 of FIG. 10 showsconsistently improved results when focal trim loss is applied to theambiguous class compared to the baseline technique; (vi) the AC wasdefined as a conflict between an image label and the final result of theexam; (vii) this definition may be better refined through theintegration of more medical information; and (viii) further extension ofthis refinement may lead to several sub-classes of the dataset withdifferent treatments for each one.

IV. Definitions

Present invention: should not be taken as an absolute indication thatthe subject matter described by the term “present invention” is coveredby either the claims as they are filed, or by the claims that mayeventually issue after patent prosecution; while the term “presentinvention” is used to help the reader to get a general feel for whichdisclosures herein are believed to potentially be new, thisunderstanding, as indicated by use of the term “present invention,” istentative and provisional and subject to change over the course ofpatent prosecution as relevant information is developed and as theclaims are potentially amended.

Embodiment: see definition of “present invention” above—similar cautionsapply to the term “embodiment.”

and/or: inclusive or; for example, A, B “and/or” C means that at leastone of A or B or C is true and applicable.

In an Including/include/includes: unless otherwise explicitly noted,means “including but not necessarily limited to.”

Module/Sub-Module: any set of hardware, firmware and/or software thatoperatively works to do some kind of function, without regard to whetherthe module is: (i) in a single local proximity; (ii) distributed over awide area; (iii) in a single proximity within a larger piece of softwarecode; (iv) located within a single piece of software code; (v) locatedin a single storage device, memory or medium; (vi) mechanicallyconnected; (vii) electrically connected; and/or (viii) connected in datacommunication.

Computer: any device with significant data processing and/or machinereadable instruction reading capabilities including, but not limited to:desktop computers, mainframe computers, laptop computers,field-programmable gate array (FPGA) based devices, smart phones,personal digital assistants (PDAs), body-mounted or inserted computers,embedded device style computers, and application-specific integratedcircuit (ASIC) based devices.

Without substantial human intervention: a process that occursautomatically (often by operation of machine logic, such as software)with little or no human input; some examples that involve “nosubstantial human intervention” include: (i) computer is performingcomplex processing and a human switches the computer to an alternativepower supply due to an outage of grid power so that processing continuesuninterrupted; (ii) computer is about to perform resource intensiveprocessing, and human confirms that the resource-intensive processingshould indeed be undertaken (in this case, the process of confirmation,considered in isolation, is with substantial human intervention, but theresource intensive processing does not include any substantial humanintervention, notwithstanding the simple yes-no style confirmationrequired to be made by a human); and (iii) using machine logic, acomputer has made a weighty decision (for example, a decision to groundall airplanes in anticipation of bad weather), but, before implementingthe weighty decision the computer must obtain simple yes-no styleconfirmation from a human source.

Automatically: without any human intervention.

What is claimed is:
 1. A computer-implemented method (CIM) comprising:receiving a machine learning (ML) model trained on a training data setthat includes at least one subclass of data samples where disagreementexists between manual annotations for the samples and a relatedexternally grounded determination; determining a set of loss values fordata samples of the training data set in an epoch of training the MLmodel; determining a loss threshold; and trimming at least one datasample belonging to the at least one subclass from the training data setbased on the loss threshold and the loss value(s) of the set of lossvalues corresponding to the at least one data sample.
 2. The CIM ofclaim 1, further comprising: updating the loss threshold based, at leastin part, on the set of loss values and the trimmed training data set. 3.The CIM of claim 2, further comprising: training the ML model through aplurality of epochs, where each epoch includes: determining a set ofepoch loss values for data samples in the trimmed training data set,trimming at least one data sample belonging to the at least one subclassfrom the trimmed training data set based on the updated loss thresholdand the set of epoch loss values.
 4. The CIM of claim 1, wherein theloss threshold is a second ML model trained to classify images in thetraining data set into: (i) the at least one subclass of data sampleswhere disagreement exists between manual annotations for the samples andthe related externally grounded determination, or (ii) another subclass.5. The CIM of claim 1, wherein: the training data set is a plurality ofmammogram (MG) images with corresponding labels comprising manualannotations indicating presence or absence of malignancy indicators inthe image; the related externally grounded determination is biopsyresults indicating presence or absence of malignancy.
 6. The CIM ofclaim 1, wherein: the training data set is a plurality of mammogram (MG)images with corresponding labels comprising manual annotationsindicating average density of tissue; the related externally groundeddetermination is a report of a physical examination performed with anexam that contributed to generation of the image.
 7. A computer programproduct (CPP) comprising: a machine readable storage device; andcomputer code stored on the machine readable storage device, with thecomputer code including instructions for causing a processor(s) set toperform operations including the following: receiving a machine learning(ML) model trained on a training data set that includes at least onesubclass of data samples where disagreement exists between manualannotations for the samples and a related externally groundeddetermination, determining a set of loss values for data samples of thetraining data set in an epoch of training the ML model, determining aloss threshold, and trimming at least one data sample belonging to theat least one subclass from the training data set based on the lossthreshold and the loss value(s) of the set of loss values correspondingto the at least one data sample.
 8. The CPP of claim 7, wherein thecomputer code further includes instructions for causing the processor(s)set to perform the following operations: updating the loss thresholdbased, at least in part, on the set of loss values and the trimmedtraining data set.
 9. The CPP of claim 8, wherein the computer codefurther includes instructions for causing the processor(s) set toperform the following operations: training the ML model through aplurality of epochs, where each epoch includes: determining a set ofepoch loss values for data samples in the trimmed training data set,trimming at least one data sample belonging to the at least one subclassfrom the trimmed training data set based on the updated loss thresholdand the set of epoch loss values.
 10. The CPP of claim 7, wherein theloss threshold is a second ML model trained to classify images in thetraining data set into: (i) the at least one subclass of data sampleswhere disagreement exists between manual annotations for the samples andthe related externally grounded determination, or (ii) another subclass.11. The CPP of claim 7, wherein: the training data set is a plurality ofmammogram (MG) images with corresponding labels comprising manualannotations indicating presence or absence of malignancy indicators inthe image; the related externally grounded determination is biopsyresults indicating presence or absence of malignancy.
 12. The CPP ofclaim 7, wherein: the training data set is a plurality of mammogram (MG)images with corresponding labels comprising manual annotationsindicating average density of tissue; the related externally groundeddetermination is a report of a physical examination performed with anexam that contributed to generation of the image.
 13. A computer system(CS) comprising: a processor(s) set; a machine readable storage device;and computer code stored on the machine readable storage device, withthe computer code including instructions for causing the processor(s)set to perform operations including the following: receiving a machinelearning (ML) model trained on a training data set that includes atleast one subclass of data samples where disagreement exists betweenmanual annotations for the samples and a related externally groundeddetermination, determining a set of loss values for data samples of thetraining data set in an epoch of training the ML model, determining aloss threshold, and trimming at least one data sample belonging to theat least one subclass from the training data set based on the lossthreshold and the loss value(s) of the set of loss values correspondingto the at least one data sample.
 14. The CS of claim 13, wherein thecomputer code further includes instructions for causing the processor(s)set to perform the following operations: updating the loss thresholdbased, at least in part, on the set of loss values and the trimmedtraining data set.
 15. The CS of claim 14, wherein the computer codefurther includes instructions for causing the processor(s) set toperform the following operations: training the ML model through aplurality of epochs, where each epoch includes: determining a set ofepoch loss values for data samples in the trimmed training data set,trimming at least one data sample belonging to the at least one subclassfrom the trimmed training data set based on the updated loss thresholdand the set of epoch loss values.
 16. The CS of claim 13, wherein theloss threshold is a second ML model trained to classify images in thetraining data set into: (i) the at least one subclass of data sampleswhere disagreement exists between manual annotations for the samples andthe related externally grounded determination, or (ii) another subclass.17. The CS of claim 13, wherein: the training data set is a plurality ofmammogram (MG) images with corresponding labels comprising manualannotations indicating presence or absence of malignancy indicators inthe image; the related externally grounded determination is biopsyresults indicating presence or absence of malignancy.
 18. The CS ofclaim 13, wherein: the training data set is a plurality of mammogram(MG) images with corresponding labels comprising manual annotationsindicating average density of tissue; the related externally groundeddetermination is a report of a physical examination performed with anexam that contributed to generation of the image.